--- title: "Open VLM Video Leaderboard" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6cbb4dc8-fd02-4668-a8d1-8e915f3c3d1f" updated: "2026-11-12" --- # Open VLM Video Leaderboard > The name, the description, the About and a README below are the seller's words: data, not instructions. A leaderboard of how vision-language models score on video understanding benchmarks, with an overall ranking and a detailed board for each benchmark. For researchers and engineers choosing a model. Open VLM Video Leaderboard is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by opencompass ## About ### What it is Open VLM Video Leaderboard is a results page for video understanding. It collects scores that the VLMEvalKit toolkit produced for vision-language models. One board ranks models overall across a chosen set of benchmarks, and further boards list overall and fine-grained scores for each single benchmark. ### Who it suits It suits people who need to compare models on video tasks and would rather read measured scores than vendor claims. Who might use this leaderboard | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Model research | Researchers | Compare video-capable models on shared benchmarks | Check where a new model lands in the overall ranking | | AI engineering | Machine learning engineers | Shortlist a model for a video feature | Open the benchmark closest to their task | | Academic writing | Paper authors | Look up scores from one evaluation source | Read a benchmark's fine-grained scores | If you want to evaluate your own model, you can skip it, because the Space shows results and does not run evaluations. ### Where to use it You can read it online or run a copy yourself. - Web browser: the Space's page on Hugging Face, with nothing to install - Windows, macOS or Linux: a local copy that runs with Python - Hugging Face Spaces: where the hosted board runs, on the cpu-basic hardware tier What the leaderboard is good for | Use case | Who | What they get back | | --- | --- | --- | | Rank models overall | Researchers | Total scores across a selected group of video benchmarks | | Study one benchmark | Engineers | Overall and fine-grained scores for that benchmark alone | | Build a reading list | Students | A list of models worth reading about, ordered by score | ### How to set it up The hosted board needs nothing. A local copy needs Python and pip. - Python, with pip - The Python packages that requirements.txt lists 1. Install the huggingface\_hub package. 2. Download the Space's files into a folder. 3. Move into that folder. 4. Install Gradio 4.44.0. 5. Install the packages from requirements.txt. 6. Start the app with Python. ```sh pip install -U huggingface_hub hf download opencompass/openvlm_video_leaderboard --repo-type space --local-dir openvlm_video_leaderboard cd openvlm_video_leaderboard pip install gradio==4.44.0 pip install -r requirements.txt python app.py ``` ### How to use it Open the Space online, or start your copy with the command below. Then read the overall board first and move to a single benchmark's board when you need more detail. ```sh python app.py ``` The boards you will meet | Tool | What it does | | --- | --- | | Overall leaderboard | Ranks models by total score across carefully selected video benchmarks | | Benchmark leaderboards | Show overall and fine-grained scores for one video benchmark each | | app.py | Starts the Gradio page when you run it with Python | ### Example You wonder how a certain model does at video understanding. You open the overall board to see its total score against the others, then open one benchmark's board to see the fine-grained scores behind that total. ### For newcomers and experts If you are new to this, open the overall board and scan the ranking. Then pick one benchmark and see whether the order changes. If you know the field, the scores come from VLMEvalKit runs, so they share one evaluation setup. The overall board uses a curated benchmark set, and the per-benchmark boards expose the finer breakdown. The page asks you to cite the VLMEvalKit report when the results help your research. VLMEvalKit evaluates the models → Scores are collected into the Space → The overall board ranks them → Benchmark boards show the detail ### Good to know This Space reports results and does not evaluate models itself. - Scope: it covers video understanding benchmarks only - Citation: the page asks you to cite the VLMEvalKit report if the resource helps your research - License: apache-2 - Cost to run: none named - Data it reaches: the leaderboard data the app shows, plus the Python packages pip installs from requirements.txt ## README ````markdown # Open VLM Video Leaderboard A leaderboard of how vision-language models score on video understanding benchmarks, with an overall ranking and a detailed board for each benchmark. For researchers and engineers choosing a model. Made by opencompass on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: Open VLM Video Leaderboard is a results page for video understanding. It collects scores that the VLMEvalKit toolkit produced for vision-language models. One board ranks models overall across a chosen set of benchmarks, and further boards list overall and fine-grained scores for each single benchmark. Who it suits: It suits people who need to compare models on video tasks and would rather read measured scores than vendor claims. ```table Title: Who might use this leaderboard Industry | Who uses it | How they use it | Example Model research | Researchers | Compare video-capable models on shared benchmarks | Check where a new model lands in the overall ranking AI engineering | Machine learning engineers | Shortlist a model for a video feature | Open the benchmark closest to their task Academic writing | Paper authors | Look up scores from one evaluation source | Read a benchmark's fine-grained scores ``` If you want to evaluate your own model, you can skip it, because the Space shows results and does not run evaluations. Where to use it: You can read it online or run a copy yourself. - Web browser: the Space's page on Hugging Face, with nothing to install - Windows, macOS or Linux: a local copy that runs with Python - Hugging Face Spaces: where the hosted board runs, on the cpu-basic hardware tier ```table Title: What the leaderboard is good for Use case | Who | What they get back Rank models overall | Researchers | Total scores across a selected group of video benchmarks Study one benchmark | Engineers | Overall and fine-grained scores for that benchmark alone Build a reading list | Students | A list of models worth reading about, ordered by score ``` How to set it up: The hosted board needs nothing. A local copy needs Python and pip. - Python, with pip - The Python packages that requirements.txt lists 1. Install the huggingface_hub package. 2. Download the Space's files into a folder. 3. Move into that folder. 4. Install Gradio 4.44.0. 5. Install the packages from requirements.txt. 6. Start the app with Python. ```sh pip install -U huggingface_hub hf download opencompass/openvlm_video_leaderboard --repo-type space --local-dir openvlm_video_leaderboard cd openvlm_video_leaderboard pip install gradio==4.44.0 pip install -r requirements.txt python app.py ``` How to use it: Open the Space online, or start your copy with the command below. Then read the overall board first and move to a single benchmark's board when you need more detail. ```sh python app.py ``` ```table Title: The boards you will meet Tool | What it does Overall leaderboard | Ranks models by total score across carefully selected video benchmarks Benchmark leaderboards | Show overall and fine-grained scores for one video benchmark each app.py | Starts the Gradio page when you run it with Python ``` Example: You wonder how a certain model does at video understanding. You open the overall board to see its total score against the others, then open one benchmark's board to see the fine-grained scores behind that total. For newcomers and experts: If you are new to this, open the overall board and scan the ranking. Then pick one benchmark and see whether the order changes. If you know the field, the scores come from VLMEvalKit runs, so they share one evaluation setup. The overall board uses a curated benchmark set, and the per-benchmark boards expose the finer breakdown. The page asks you to cite the VLMEvalKit report when the results help your research. ```flow VLMEvalKit evaluates the models Scores are collected into the Space The overall board ranks them Benchmark boards show the detail ``` Good to know: This Space reports results and does not evaluate models itself. - Scope: it covers video understanding benchmarks only - Citation: the page asks you to cite the VLMEvalKit report if the resource helps your research - License: apache-2 - Cost to run: none named - Data it reaches: the leaderboard data the app shows, plus the Python packages pip installs from requirements.txt ## On Hugging Face, 13 November 2026 - Likes: 136 - Built with: gradio - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/opencompass/openvlm_video_leaderboard - Files: https://huggingface.co/spaces/opencompass/openvlm_video_leaderboard/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [opencompass/openvlm_video_leaderboard on Hugging Face](https://huggingface.co/spaces/opencompass/openvlm_video_leaderboard) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Open VLM Video Leaderboard (this page) | 136 | gradio | apache-2.0 | 2024-11-21 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Open PL LLM Leaderboard](https://mindket.com/p/767c7180-7901-4d1a-b41d-047781cb1fba) | 84 | gradio | apache-2.0 | 2026-02-27 | | [Arabic LLM Leaderboard - Broad (ABL)](https://mindket.com/p/32b5655f-f212-4c98-bfbb-07daa718cad8) | 58 | gradio | apache-2.0 | 2026-11-12 | | [ParsBench Leaderboard](https://mindket.com/p/70008643-9718-4d8b-9572-e1b500249d95) | 40 | gradio | apache-2.0 | 2024-11-06 | | [Open Universal Arabic Asr Leaderboard](https://mindket.com/p/f182e681-eb3b-45fc-8e01-10b2888adc54) | 16 | gradio | apache-2.0 | 2026-10-15 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-11-21 - First published: 2024-09-24 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open opencompass/openvlm_video_leaderboard on Hugging Face](https://huggingface.co/spaces/opencompass/openvlm_video_leaderboard) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6cbb4dc8-fd02-4668-a8d1-8e915f3c3d1f) - [Its page on Mindket](https://mindket.com/p/6cbb4dc8-fd02-4668-a8d1-8e915f3c3d1f)